Merge branch 'master' of github.com:h2oai/h2o

dejurin · Apr 17, 2015 · 18748e9 · 18748e9
2 parents fcb8e43 + 103b4d0
commit 18748e9
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diff --git a/R/tests/testdir_jira/runit_hex_2020_LR_beta_constraints.R b/R/tests/testdir_jira/runit_hex_2020_LR_beta_constraints.R
@@ -45,7 +45,10 @@ test.LR.betaConstraints <- function(conn) {
     upper_bound = -0.002
     beta_age$lower_bounds = lower_bound
     beta_age$upper_bounds = upper_bound
+    nrow_prior = nrow(prostate.hex)
     lr_negativeUpper.h2o = h2o.glm(x = "AGE", y = "CAPSULE", data = prostate.hex, family = family_type, alpha = 0, beta_constraints = beta_age, standardize = T)
+    nrow_after = nrow(prostate.hex)
+    if(!nrow_prior == nrow_after) stop("H2OParsedData object is being overwritten.")
 
     Log.info("Shift AGE column to reflect negative upperbound...")
     xDataFrame = data.frame(AGE = prostate.csv[,"AGE"]*(1+upper_bound), Intercept = intercept)

diff --git a/h2o-docs/source/conf.py b/h2o-docs/source/conf.py
@@ -56,7 +56,7 @@
 
 # General information about the project.
 project = u'H2O Documentation'
-copyright = u'2013, 0xdata, Inc'
+copyright = u'2015, H2O.ai'
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the
@@ -269,7 +269,7 @@
 epub_title = u'h2o'
 epub_author = u'H2O Open Source Team'
 epub_publisher = u'H2O Open Source Team'
-epub_copyright = u'2013, H2O Open Source Team'
+epub_copyright = u'2015, H2O Open Source Team'
 
 # The language of the text. It defaults to the language option
 # or en if the language is not set.

diff --git a/h2o-docs/source/datascience/deeplearning.rst b/h2o-docs/source/datascience/deeplearning.rst
@@ -528,13 +528,16 @@ References
 
     Feature Importance http://www.ncbi.nlm.nih.gov/pubmed/9327276
 
-    Deep Learning Vignette http://h2o.gitbooks.io/h2o-deep-learning/
+    Deep Learning Vignette https://leanpub.com/deeplearning
 
     Deep Learning Training http://learn.h2o.ai/content/hands-on_training/deep_learning.html
 
-   Niu, Feng, et al. "Hogwild!: A lock-free approach to parallelizing
-stochastic gradient descent." Advances in Neural Information
-Processing Systems 24 (2011): 693-701. (algorithm implemented is on p.5)
-https://papers.nips.cc/paper/4390-hogwild-a-lock-free-approach-to-parallelizing-stochastic-gradient-descent.pdf
+    Slideshare slide decks http://www.slideshare.net/0xdata/presentations?order=latest
+
+    Youtube channel https://www.youtube.com/user/0xdata
+
+    The Definitive Performance Tuning Guide for H2O Deep Learning http://h2o.ai/blog/2015/02/deep-learning-performance/
+
+	Niu, Feng, et al. "Hogwild!: A lock-free approach to parallelizing stochastic gradient descent." Advances in Neural Information Processing Systems 24 (2011): 693-701. (algorithm implemented is on p.5) https://papers.nips.cc/paper/4390-hogwild-a-lock-free-approach-to-parallelizing-stochastic-gradient-descent.pdf
 
 """"""